The data generated by source devices and distributed to multiple nodes may contain sensitive information that should not be shared with untrusted parties (e.g., images, GPS coordinates, and audio.).
However, the IoT system is composed of uncontrolled devices that may attack the input fed by the previous helpers and expose the system to privacy issues. For example, authors in [1]} presented some potential attacks and vulnerabilities menacing the collaborative inference, including black-box attacks. In black-box settings, the attacker trains a DNN network that maps the received features to the targeted input in order to obtain the original sensitive data. Attacking the inference through an inverse network is feasible and more efficient, when the neural system is distributed into layers as done in [2]}.
